Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) cells is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as highways for gradients, which can pass underlying information directly to the upper layer, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked deeply) dimensions, aiming to enhance the recognition rate.When tested with the Opportunity data set and the public domain UCI data set, the accuracy was increased by 4.78% and 3.68%, respectively, compared with previously reported results. Finally, the confusion matrix of the public domain UCI data set was analyzed. IntroductionIn real life, many problems can be described as time series problems. Indeed, human activity recognition (HAR) is of value in both theoretical research and actual practice. It can be used widely, including in health monitoring [1][2], smart homes [3][4], and human-computer interactions [5][6]; for example, LSTM cells are a good choice for solving HAR problems. Unlike traditional algorithms, LSTM can catch relationships in data on the temporal dimension without having to mix the time steps together as a 1D convolutional neural network (CNN) would do. As more of what is commonly called "big data" emerges, LSTM architecture can offer great performance and many potential applications. More specifically, HAR is the process of obtaining action data with sensors; it symbolizes the action information and then allows understanding and extraction of the motion characteristics, which is what activity recognition refers to. Because of the spatial complexity and temporal divergence of behavior, there is no unified recognition method. A public domain benchmark of HAR has been introduced, and different methods of recognition have been analyzed [7]. The results showed that the K-Nearest Neighbor (KNN) algorithm outperforms other algorithms in most recognition tasks. Support Vector Machine (SVM) is another outstanding algorithm. A Multi-Class Hardware-Friendly Support Vector Machine (MC-HF-SVM), which uses fixed-point arithmetic for HAR instead of the typical floating-point arithmetic, has been proposed for sensor data [8]. Unlike the manual filtering features in previous algorithms, a systematic feature learning method that combines feature extraction with CNN training has also been proposed [9]. Subsequently, DeepConvLSTM networks outperformed previous algorithms in the Opportunity Challenge by an average of 4% of the F1 score [10]; the effects of parameters on the final result were...

Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data.In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories.

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Beyond-visual-range (BVR) engagement becomes more and more popular in the modern air battlefield. The key and difficulty for pilots in the fight is maneuver planning, which reflects the tactical decision-making capacity of the both sides and determinates success or failure. In this paper, we propose an intelligent maneuver planning method for BVR combat with using an improved deep Q network (DQN). First, a basic combat environment builds, which mainly includes flight motion model, relative motion model and missile attack model. Then, we create a maneuver decision framework for agent interaction with the environment. Basic perceptive variables are constructed for agents to form continuous state space. Also, considering the threat of each side missile and the constraint of airfield, the reward function is designed for agents to training. Later, we introduce a training algorithm and propose perceptional situation layers and value fitting layers to replace policy network in DQN. Based on long short-term memory (LSTM) cell, the perceptional situation layer can convert basic state to high-dimensional perception situation. The fitting layer does well in mapping action. Finally, three combat scenarios are designed for agent training and testing. Simulation result shows the agent can avoid the threat of enemy and gather own advantages to threat the target. It also proves the models and methods of agents are valid and intelligent air combat can be realized.

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots' description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

In order to exploit the enhancement of the multiobjective evolutionary algorithm based on decomposition (MOEA/D), we propose an improved algorithm with uniform design (UD), i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modi ed by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the relationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEA/D with adaptive weight adjustment (MOEA/D-AWA), a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEA/D-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the differential evolution operator (MOEA/D-DE), MOEA/D-AWA and the nondominated sorting genetic algorithm II (NSGA-II) on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversi ed set of solutions within an acceptable execution time.

This paper addresses the problem of suppression of the integrated air defense system (IADS) by multiple fighters' cooperation. Considering the dynamic changing of the number of the nodes in the operational process, a profit model for the influence of the mission's cost for the whole system is developed for both offense and defensive sides. The scenario analysis is given for the process of suppressing the IADS by multiple fighters. Based on this scenario analysis, the modeling method and the specific expression for the payoff function are proposed in four cases for each node. Moreover, a distributed virtual learning algorithm is designed for the n-person and n-strategy game, and the mixed strategy Nash equilibrium (MSNE) of this game can be solved from the n × m × 3-dimensional profit space. Finally, the simulation examples are provided to demonstrate the effectiveness of the proposed model and the game algorithm.

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.